Abstract 5905: User characteristics of “Cook For Your Life” - a website designed to support cancer patients and survivors
Bibliographic record
Abstract
Abstract Purpose: Cook for Your Life (cookforyourlife.org) is a bilingual, science-based nutrition and culinary website designed for cancer patients and survivors based at the Fred Hutchinson Cancer Research Center. The website has been used as a tool and resource for health intervention research. This analysis described the characteristics of English and Spanish language users who responded to an online survey. Methods: Visitors to cookforyourlife.org were invited to participate in an online survey collecting demographic characteristics and health behaviors. Those at least 18 years old were eligible. Respondents with a cancer diagnosis were asked a subset of questions about treatment and side effects. English language (EL) and Spanish language (SL) versions launched in December 2020 and April 2021, respectively. Survey data were analyzed through October 2021 and only included those completing at least 50% of the survey questions. Demographic characteristics from web analytics data were compared. Results: Among EL respondents, 3039 initiated the survey and 2417 completed at least 50% of the questions. Of these, 53% were persons with a cancer diagnosis, 8% were caregivers of cancer patients, and 39% other. The majority of EL respondents were US residents (77%), but many were also from Europe (11%) and Canada (6%). Cancer patients/survivors were most likely to be ≥55 years old, female, non-Hispanic white, have income >$100K, and be college educated. Caregivers and others were younger, but otherwise had similar demographics. Among cancer patients/survivors, 46% had breast cancer and 7% pancreatic and 49% reported having treatment side effects in the past week, with 31% citing fatigue and 15% anxiety. Among SL respondents, 804 initiated the survey and 545 were eligible for analysis. Of these, 17% were cancer patients/survivors, 8% caregivers, and 75% other. SL respondents were also more likely to be female and highly educated, but were younger, from South/Latin America, and had income <$30K. Among SL cancer survivors, 31% had breast cancer and 8% had colorectal. Web analytics data on 1.5+ million visitors from December 2020 to October 2021 indicated most visitors were 71% female and lived in South/Latin America (35%) or the US (31%). Conclusions: Respondents of the Cook for Your Life English-language website survey were predominantly US women with high socioeconomic status; many had history of breast cancer. Conversely, Spanish-language respondents had more socioeconomic diversity, but fewer were diagnosed with cancer. Web analytics data suggested survey respondents may differ demographically from general website users. Knowledge about our website users is necessary for developing targeted strategies to improve reach. Future research efforts will focus on delivering content to more varied populations of cancer patients and survivors, their caregivers, and individuals interested in cancer prevention. Citation Format: Eileen Rillamas-Sun, Liza Schattenkerk, Sofia Cobos, Kate Ueland, Heather Greenlee. User characteristics of “Cook For Your Life” - a website designed to support cancer patients and survivors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5905.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".